Open Access

ARTICLE

Phishing Attacks Detection Using Ensemble Machine Learning Algorithms

by Nisreen Innab¹, Ahmed Abdelgader Fadol Osman², Mohammed Awad Mohammed Ataelfadiel², Marwan Abu-Zanona^3,*, Bassam Mohammad Elzaghmouri⁴, Farah H. Zawaideh⁵, Mouiad Fadeil Alawneh⁶

1 Department of Computer Science and Information Systems, College of Applied Sciences, Almaarefa University, Diriyah, Riyadh, 13713, Saudi Arabia
2 Applied College, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
3 Department of Management Information Systems, College of Business Administration, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
4 Department of Computer Science, Faculty of Computer Science and Information Technology, Jerash University, Jerash, 26110, Jordan
5 Department of Business Intelligence and Data Analysis, Faculty of Financial Sciences and Business, Irbid National University, Irbid, 21110, Jordan
6 Faculty of Information Technology, Ajloun National University, Ajloun, 26767, Jordan

* Corresponding Author: Marwan Abu-Zanona. Email:

Computers, Materials & Continua 2024, 80(1), 1325-1345. https://doi.org/10.32604/cmc.2024.051778

Received 15 March 2024; Accepted 03 June 2024; Issue published 18 July 2024

Abstract

Phishing, an Internet fraud where individuals are deceived into revealing critical personal and account information, poses a significant risk to both consumers and web-based institutions. Data indicates a persistent rise in phishing attacks. Moreover, these fraudulent schemes are progressively becoming more intricate, thereby rendering them more challenging to identify. Hence, it is imperative to utilize sophisticated algorithms to address this issue. Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors. Machine learning (ML) approaches can identify common characteristics in most phishing assaults. In this paper, we propose an ensemble approach and compare it with six machine learning techniques to determine the type of website and whether it is normal or not based on two phishing datasets. After that, we used the normalization technique on the dataset to transform the range of all the features into the same range. The findings of this paper for all algorithms are as follows in the first dataset based on accuracy, precision, recall, and F1-score, respectively: Decision Tree (DT) (0.964, 0.961, 0.976, 0.968), Random Forest (RF) (0.970, 0.964, 0.984, 0.974), Gradient Boosting (GB) (0.960, 0.959, 0.971, 0.965), XGBoost (XGB) (0.973, 0.976, 0.976, 0.976), AdaBoost (0.934, 0.934, 0.950, 0.942), Multi Layer Perceptron (MLP) (0.970, 0.971, 0.976, 0.974) and Voting (0.978, 0.975, 0.987, 0.981). So, the Voting classifier gave the best results. While in the second dataset, all the algorithms gave the same results in four evaluation metrics, which indicates that each of them can effectively accomplish the prediction process. Also, this approach outperformed the previous work in detecting phishing websites with high accuracy, a lower false negative rate, a shorter prediction time, and a lower false positive rate.

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Keywords

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